Financial Social Network
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This financial social network features financial information for many corporations, historical and stock, that can be represented in charts. The charts can be overlapped and they can contain annotations related to various events. Users can monitor or exchange custom charts.
Back-end
The back-end is a custom Django deployment, PostgreSQL database and Debian server operating system.
The standard for information as a block of text, XBRL provides a computer-readable tag to identify each individual item of data. By attaching identifying tags to individual pieces of data, a business reporting document becomes “intelligent” data, allowing the exchange of business data by encoding the information in a meaningful way.
XBRL has two major components: taxonomy and instance documents.
The taxonomy describes the business concepts, called elements, and the relations between them. A taxonomy is divided in a schema that provides basic metadata for the elements and linkbases - collections of links/pointers to centralized documents that describe labels (used for translation), references (for legislation, committee decisions, literature, etc.), calculations (formulas used to produce/verify the data), definitions and presentation hints.
The instance document is the actual financial data expressed according to the conventions of a taxonomy. These business facts are described using elements from the taxonomy, units, a context (the company usually) and a value. The XBRL instance is processed, RDF triples are extracted and stored in the database. RDF is a standard model for data interchange on the Web. RDF extends the linking structure of the Web to use URIs to name the relationship between things as well as the two ends of the link. This linking structure forms a directed, labeled graph, where the edges represent the named link between two resources, represented by the graph nodes.
Having the financial data stored in RDF, we used SPARQL to access it. SPARQL is a query language and a protocol for accessing RDF. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. SPARQL contains capabilities for querying required and optional graph patterns along with their conjunctions and disjunctions. SPARQL also supports extensible value testing and constraining queries by source RDF graph. The results of SPARQL queries can be results sets or RDF graphs.
Front-end
The interface is carefully designed to follow all the client’s requests. 960 Grid System and jQuery help us ensure good functioning and appearance across all major browsers, yet allow us the freedom to customize the user experience.
The charts are crafted with great care and make great use of what Adobe's Flex brings to the table in order to yield a smooth user experience in the harsh matter of financial studies. We're dealing with decades of financial information. There's literally a mountain of data on our side and there's a mountain of curiosity on the user's side. The charts are there to bridge the two mountains. Because of this, they have been pampered beyond belief to make them what you see today.
Features
XBRL viewer
For a clear and elegant approach regarding all the available corporations together with their values we implemented an XBRL viewer. The XBRL viewer permits reviewing, in detail, an XBRL instance document. Here is a screenshot of the viewer:
Social network
The user profile page encapsulates elements from micro-blogging to activity feeds and user specific charts display. The jeditable plugin allowed us to implement an elegant edit-in-place system for profile, account and privacy settings.
Charts
Data is loaded dynamically, according to the zoom level or to the time period selected. Annotations are dynamically added based on RSS feeds or custom input.
Dashboards
A user can define a number of dashboards. Each dashboard can display up to 8 charts that are monitored at one time. Beneath the charts there is a list of relevant RSS news.
